search for: qi_a

...d in the 1st layer. Caused by error in `ans[ypos] <- rep(yes, length.out = len)[ypos]`: ! replacement has length zero Run `rlang::last_trace()` to see where the error occurred. Warning message: In rep(yes, length.out = len) : 'x' is NULL so the result will be NULL Data Farm_ID Jahr Bio QI_A 1 2015 1 9.5 2 2018 1 15.7 3 2020 1 21.5 1 2015 1 50.5 2 2018 1 12.9 3 2020 1 11.2 1 2015 1 30.6 2 2018 1 28.7 3 2020 1 29.8 1 2015 1 30.1 2 2018 1 NA 3 2020 1 16.9 1 2015 0 6.5 2 2018 0 7.9 3 2020 0 10.2 1 2015 0 11.2 2 2018 0 18.5 3 2020 0 29.5 1 2015 0 25.1 2 2018 0 16.1 3 2020 0 15.9 1 2015 0 1...

...command ?notch=TRUE? is at the wrong place in my special code > construct? > > > > Without notch=TRUE the code provides the planned ggplot. > > > > Kind regards > > Sibylle > > > > Code: > > > > MS1<- MS %>% filter(QI_A!="NA") %>% droplevels() > > MS1$Jahr<-as.factor(MS1$Jahr) > > > > MS1s <- MS1 %>% > > group_by(MS1$Jahr, MS1$Bio) %>% > > summarise( > > y0 = quantile(QI_A, 0.05), > > y25 = quantile(QI_A, 0.25), > >...

...mmunity I tried the following code using geom_boxplot() and notch=TRUE. Does anyone know if the command ?notch=TRUE? is at the wrong place in my special code construct? Without notch=TRUE the code provides the planned ggplot. Kind regards Sibylle Code: MS1<- MS %>% filter(QI_A!="NA") %>% droplevels() MS1$Jahr<-as.factor(MS1$Jahr) MS1s <- MS1 %>% group_by(MS1$Jahr, MS1$Bio) %>% summarise( y0 = quantile(QI_A, 0.05), y25 = quantile(QI_A, 0.25), y50 = mean(QI_A), y75 = quantile(QI_A, 0.75), y100 = quantile(QI_A, 0....

...E ST?CKLI via R-help < r-help at r-project.org> wrote: > Dear community > > > > Using after_stat() I was able to visualise ggplot with standard deviations > instead of a confidence interval as seen in the R help. > > > > p1<-ggplot(data = MS1, aes(x= Jahr, y= QI_A,color=Bio, linetype=Bio)) + > > geom_smooth(aes(fill=Bio, > ymax=after_stat(y+se*sqrt(length(y))), ymin=after_stat(y-se*sqrt(y))) , > method = "lm" , formula = y ~ x + I(x^2),linewidth=1) + > > theme(panel.background = element_blank())+ &g...

...o do in the geom_boxplot() call and how it uses the summarised data tibble. It may even be that if you do that, you will find what's causing the problem!? (I speak from bitter experience!!) Very best (all), Chris On 16/08/2024 17:51, SIBYLLE ST?CKLI via R-help wrote: > Farm_ID Jahr Bio QI_A > 1 2015 1 9.5 > 2 2018 1 15.7 > 3 2020 1 21.5 > 1 2015 1 50.5 > 2 2018 1 12.9 > 3 2020 1 11.2 > 1 2015 1 30.6 > 2 2018 1 28.7 > 3 2020 1 29.8 > 1 2015 1 30.1 > 2 2018 1 NA > 3 2020 1 16.9 > 1 2015 0 6.5 > 2 2018 0 7.9 > 3 2020 0 10.2 > 1 2015 0 11.2...

Dear community Using after_stat() I was able to visualise ggplot with standard deviations instead of a confidence interval as seen in the R help. p1<-ggplot(data = MS1, aes(x= Jahr, y= QI_A,color=Bio, linetype=Bio)) + geom_smooth(aes(fill=Bio, ymax=after_stat(y+se*sqrt(length(y))), ymin=after_stat(y-se*sqrt(y))) , method = "lm" , formula = y ~ x + I(x^2),linewidth=1) + theme(panel.background = element_blank())+ theme(axis.l...

...inetype (solid and dashed). Challenge: my code produces now two legends. One with the colors for the group and one with the linetype for the group. Does somebody have a hint how to adapt the code to produce one legend? Group 0 = red and dashed, Group 1 = green and solid? MS1<- MS %>% filter(QI_A!="NA") %>% droplevels() dev.new(width=4, height=2.75) par(mar = c(0,6,0,0)) p1<-ggplot(data = MS1, aes(x= Jahr, y= QI_A,group=Bio,color=Bio, linetype=Bio)) + geom_smooth(aes(fill=Bio) , method = "lm" , formula = y ~ x + I(x^2),linewidth=1) + theme(panel.background =...

...allenge: my code produces now two legends. One with the colors for the > group and one with the linetype for the group. Does somebody have a hint how > to adapt the code to produce one legend? Group 0 = red and dashed, Group 1 = > green and solid? > > > MS1<- MS %>% filter(QI_A!="NA") %>% droplevels() > dev.new(width=4, height=2.75) > par(mar = c(0,6,0,0)) > p1<-ggplot(data = MS1, aes(x= Jahr, y= QI_A,group=Bio,color=Bio, > linetype=Bio)) + > geom_smooth(aes(fill=Bio) , method = "lm" , formula = y ~ x + > I(x^2),linewidth=...

...>Challenge: my code produces now two legends. One with the colors for the >group and one with the linetype for the group. Does somebody have a hint how >to adapt the code to produce one legend? Group 0 = red and dashed, Group 1 = >green and solid? > > >MS1<- MS %>% filter(QI_A!="NA") %>% droplevels() >dev.new(width=4, height=2.75) >par(mar = c(0,6,0,0)) >p1<-ggplot(data = MS1, aes(x= Jahr, y= QI_A,group=Bio,color=Bio, >linetype=Bio)) + > geom_smooth(aes(fill=Bio) , method = "lm" , formula = y ~ x + >I(x^2),linewidth=1) + &...

...One with the colors for the >> group and one with the linetype for the group. Does somebody have a >> hint how >> to adapt the code to produce one legend? Group 0 = red and dashed, >> Group 1 = >> green and solid? >> >> >> MS1<- MS %>% filter(QI_A!="NA") %>% droplevels() >> dev.new(width=4, height=2.75) >> par(mar = c(0,6,0,0)) >> p1<-ggplot(data = MS1, aes(x= Jahr, y= QI_A,group=Bio,color=Bio, >> linetype=Bio)) + >> ???????? geom_smooth(aes(fill=Bio) , method = "lm" , formula = y ~ x +...